Datatron’s customers can rapidly and confidently leverage AI/ML to capture new business gains
Streamline and standardize changes, monitor model performance, and correct for model degradation or decay
ML ModelOps & Governance for enterprises running dozens of models in diverse global environments
- Accelerate time to market by being able to deploy more models into production rapidly
- Avoid manual scripts and custom coding for model deployments, reducing time and effort
- Ability to keep track of all models in the enterprise
- Identify potential model drift, bias, performance, anomalies before they cause harm to the company
- Enable data scientists and IT to collaborate on production-level challenges
- Intuitive dashboard that communicates relevant insights to business owners, data scientists and IT
- Detect critical compliance issues before they occur
- Eliminate long-term supportability issues with open-source or internally built systems
- Allow business and IT to ensure interoperability with existing infrastructure
- Reduce significant capital and operation overhead to compete for talents to build custom systems
- Focus resources to solve business critical needs
Datatron helps answer important questions about ML model operations
Why is it difficult to achieve the expected ROI of AI/ML at scale?
Most people today focus on the appeal of developing AI models and have not understood the complexity of operationalizing these models. Because of such complexity, models often sit in the lab and are unable to help businesses achieve the promised ROI with AI/ML.
Why is it so difficult to operationalize AI models?
Businesses are applying the traditional software development lifecycle to manage AI/ML models, from the application layer to the middleware and to the infrastructure. However, AI/ML is a major paradigm shift whereby traditional software models do not fit. This is why you often hear businesses spending up to 12 months before a model can be deployed into production.
How to ensure models are performing as expected?
There are a few key elements needed to ensure models are performing as expected. For example, model explainability must work with real-world production data, instead of lab research, to capture potential issues. In addition to model explainability, it is also important to understand how the underlying infrastructure supports these models for the most optimal performance.
Why should I consider a commercial MLOps platform?
Putting AI/ML models into production is not trivial. It is definitely possible to build a sizable team to learn the intricacies on how to support AI/ML models developed by the data scientists. A commercial solution shortens your time-to-market, investing in areas that will help you differentiate against the rest. You avoid mounting deployment, monitoring, and optimization costs for each new model you create. Furthermore, you get enterprise-quality support when things go wrong.
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